Duncavage Eric J, Abel Haley J, Pfeifer John D
Department of Pathology, Washington University School of Medicine, St. Louis, Missouri.
Department of Genetics, Washington University School of Medicine, St. Louis, Missouri.
J Mol Diagn. 2017 Jan;19(1):35-42. doi: 10.1016/j.jmoldx.2016.09.005. Epub 2016 Nov 15.
Quality assurance for clinical next-generation sequencing (NGS)-based assays is difficult given the complex methods and the range of sequence variants such assays can detect. As the number and range of mutations detected by clinical NGS assays has increased, it is difficult to apply standard analyte-specific proficiency testing (PT). Most current proficiency testing challenges for NGS are methods-based PT surveys that use DNA from reference samples engineered to harbor specific mutations that test both sequence generation and bioinformatics analysis. These methods-based PTs are limited by the number and types of mutations that can be physically introduced into a single DNA sample. In silico proficiency testing, which evaluates only the bioinformatics component of NGS assays, is a recently introduced PT method that allows for evaluation of numerous mutations spanning a range of variant classes. In silico PT data sets can be generated from simulated or actual sequencing data and are used to test alignment through variant detection and annotation steps. In silico PT has several advantages over the use of physical samples, including greater flexibility in tested variants, the ability to design laboratory-specific challenges, and lower costs. Herein, we review the use of in silico PT as an alternative to traditional methods-based PT as it is evolving in oncology applications and discuss how the approach is applicable more broadly.
鉴于基于临床下一代测序(NGS)的检测方法复杂,且此类检测能够检测的序列变异范围广泛,其质量保证颇具难度。随着临床NGS检测所检测到的突变数量和范围不断增加,应用标准的特定分析物能力验证(PT)变得困难。目前,大多数针对NGS的能力验证挑战都是基于方法的PT调查,这些调查使用来自经过工程改造以携带特定突变的参考样本的DNA,以测试序列生成和生物信息学分析。这些基于方法的PT受到可物理引入单个DNA样本中的突变数量和类型的限制。虚拟能力验证仅评估NGS检测的生物信息学部分,是最近引入的一种PT方法,它允许评估跨越一系列变异类别的众多突变。虚拟PT数据集可以从模拟或实际测序数据生成,并用于通过变异检测和注释步骤测试比对。与使用物理样本相比,虚拟PT具有多个优点,包括在测试变异方面具有更大的灵活性、能够设计针对特定实验室的挑战以及成本更低。在此,我们回顾虚拟PT作为传统基于方法的PT的替代方法在肿瘤学应用中的发展情况,并讨论该方法如何更广泛地适用。